Essays In Econometrics Nonparametrics And Robustness
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Essays In Econometrics Nonparametrics And Robustness
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Author : Benjamin William Deaner
language : en
Publisher:
Release Date : 2021
Essays In Econometrics Nonparametrics And Robustness written by Benjamin William Deaner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Heterogeneity and my key identifying assumptions follow from restrictions on the serial dependence structure.
Three Essays On Nonparametric Econometrics With Applications To Financial Economics And Insurance
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Author : Kuangyu Wen
language : en
Publisher:
Release Date : 2015
Three Essays On Nonparametric Econometrics With Applications To Financial Economics And Insurance written by Kuangyu Wen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
This dissertation includes three essays. The first essay concerns nonparametric kernel density estimation on the unit interval. The Kernel Density Estimator (KDE) suffers boundary biases when applied to densities on bounded supports, which are assumed to be the unit interval. Transformations mapping the unit interval to the real line can be used to remove boundary biases. However, this approach may induce erratic tail behaviors when the estimated density of transformed data is transformed back to its original scale. I propose a modified transformation based KDE that employs a tapered and tilted back-transformation. I derive the theoretical properties of the new estimator and show that it asymptotically dominates the naive transformation based estimator while maintains its simplicity. I then propose three automatic methods of smoothing parameter selection. Monte Carlo simulations demonstrate the good finite sample performance of the proposed estimator, especially for densities with poles near the boundaries. An example with real data is provided. The second essay proposes a new kernel estimator of copula densities. The standard kernel estimator suffers boundary biases since copula densities are defined on a bounded support and often tend to infinity on the boundaries. A transformation based estimator aptly remedies both boundary biases and inconsistencies due to unbounded densities. This method, however, might entail undesirable boundary behaviors due to an unbounded multiplicative factor associated with the transformation. I propose a modified transformation-based estimator that employs an infinitesimal tapering device to mitigate the influence of the unbounded multiplier. I establish the asymptotic properties of our estimator and show that it dominates the original transformation estimator in terms of mean squared error due to bias correction. I present two practically simple methods of smoothing parameter selection. I further show that the proposed estimator admits higher order bias reduction for Gaussian copulas and provides outstanding performance for Gaussian and near Gaussian copulas. This appealing feature makes our estimator particularly suitable for financial data analyses. Extensive simulations corroborate our theoretical analysis and demonstrate outstanding performance of the proposed method relative to competing estimators. Three empirical applications are provided. The third essay studies nonparametric estimation of crop yield distributions and crop insurance premium rates. Since U.S. crop yield data are typically available at county level for only a few decades, nonparametric estimation of yield distribution for individual counties suffers from small sample sizes. The fact that nearby counties share similarities in their yield distributions suggests possible efficiency gains through information pooling. I propose a weighted kernel density estimator subject to selected spatial moment restrictions. The weights are calculated using the method of empirical likelihood and the spatial moments are specified based on the consideration of flexibility and robustness. I further extend the proposed method to the adaptive kernel density estimation. My simulations demonstrate the outstanding performance of the proposed methods in the estimation of crop yield distributions and that of crop insurance premium rates. I apply these methods to estimate corn yield distributions and crop insurance premium rates for the ninety-nine counties in Iowa. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155094
Recent Advances And Future Directions In Causality Prediction And Specification Analysis
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Author : Xiaohong Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-08-01
Recent Advances And Future Directions In Causality Prediction And Specification Analysis written by Xiaohong Chen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-01 with Business & Economics categories.
This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.
Essays In Honor Of Peter C B Phillips
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Author : Thomas B. Fomby
language : en
Publisher: Emerald Group Publishing
Release Date : 2014-11-21
Essays In Honor Of Peter C B Phillips written by Thomas B. Fomby and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-21 with Political Science categories.
This volume honors Professor Peter C.B. Phillips' many contributions to the field of econometrics. The topics include non-stationary time series, panel models, financial econometrics, predictive tests, IV estimation and inference, difference-in-difference regressions, stochastic dominance techniques, and information matrix testing.
Essays On Nonparametric And Dynamic Time Seies Econometrics
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Author : Shih-Tang Hwu
language : en
Publisher:
Release Date : 2018
Essays On Nonparametric And Dynamic Time Seies Econometrics written by Shih-Tang Hwu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This dissertation explores important macroeconomics issues based on both classical and Bayesian Econometrics tools developed. One goal of the first chapter of the dissertation is to develop identification conditions and algorithm for estimating Markov-switching models without imposing distribution assumptions. Since the seminal work of Hamilton (1989), the basic Markov-switching model has been extended in various ways. Without a single exception, estimation of the aforementioned models and the other Markov-switching models in the literature has relied upon parametric assumptions on the distribution of the error terms. Most applications of Markov-switching models in the literature assume normally distributed error terms, with rare exceptions like Dueker (1997) who proposes a model of stock returns in which the innovation comes from a Student-t distribution. The question then would be: what if a normal log-likelihood is maximized but the normality assumption is violated? Based on simulation studies, we find that maximum likelihood estimation could lead to sizable bias in the parameter estimates and poor inferences about regime probabilities when the normality assumption is violated, even for a sample size as large as 5,000. We approximate the unknown distribution of the error term by the Dirichlet process mixture of normals, in which the number of mixtures is treated as a parameter to estimate. In doing so, we pay a special attention to identification of the model. We apply the proposed model to the growth of postwar U.S. industrial production index in order to investigate its regime-switching dynamics. Our univariate model can effectively control for the irregular components that is not related to business conditions. This leads to sharp and accurate inferences on recession probabilities just like the dynamic factor models of Kim and Yoo (1995), Chauvet (1998), and Kim and Nelson (1998) do. The second chapter of the dissertation investigates the relationships between innovations to trend inflation and inflation-gap in a univariate unobserved components model with with Markov-switching volatility. Building on the work of Stock and Watson (2007), we empirically shows that a negative correlation between innovations to trend inflation and the inflation gap, when it is combined with time-varying inflation gap persistence, plays an important role in the dynamics of postwar US inflation. A negative correlation between trend inflation and the markup shock may be an important source of their negative correlation. Like the time-varying VAR models of Cogley and Sbordone (2008) and Ascari and Sbordone (2014), our model results in smooth trend inflation, from which inflation persistently deviates during the Great inflation period. Furthermore, our model provides superior out-of-sample forecasts than Stock and Watson's (2007) unobserved components model with stochastic volatility or than Atkeson and Ohanian's (2001) random walk model does. One goal of the last chapter of the dissertation is to develop estimation methods in linear regression model with endogenous variables but only weak instrument variables. The proposed methods exploit the time-varying volatility of the endogenous variables. We show that the proposed estimators are consistent and asymptotically normally distributed. We also show that the proposed methods have much better power compare with the existing weak instrument robust test through simulations. Another goal of the last chapter is to investigate the magnitude of elasticity of intertemporal substitution (EIS), which is one of the most important parameters in applied macroeconomics and finance. Yogo (2004) applies the existing weak instrument robust test to estimate EIS and find 22 out of 33 confidence interval to be ([-infinity, infinity])which is very uninformative. We apply proposed approach to estimate the EIS using the data employed by Yogo (2004). Confidence intervals based on proposed methods are much tighter than those constructed by weak instrument robust tests and its value is generally close to 0.
Essays On Nonparametric Structural Econometrics
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Author : Zhutong Gu
language : en
Publisher:
Release Date : 2017
Essays On Nonparametric Structural Econometrics written by Zhutong Gu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Econometrics categories.
My dissertation contains three papers in the theory and applications of nonparametric structural econometrics. In chapter 1, I propose a nonparametric test for additive separability of unobservables of unrestricted dimensions with average structural functions. Chapter 2 considers identification and estimation of fully nonparametric production functions and empirically tests for the Hicks-neutral productivity shocks, a direct application of the test proposed in chapter 1. In chapter 3, my authors and I study the semiparametric ordered response models with correlated unobserved thresholds and investigate the issue of corporate bond rating biases due to the sharing of common investors between bond-issuing firms and credit rating agencies. Brief abstracts are presented in order below. Additive separability between observables and unobservables is one of the essential properties in structural modeling of heterogeneity in the presence of endogeneity. In this chapter, I propose an easy-to-compute test based on empirical quantile mean differences between the average structural functions (ASFs) generated by nonparametric nonseparable and separable models with unrestricted heterogeneity. Given identification, I establish conditions under which structural additivity can be linked to the equality of ASFs derived from the two commonly employed competing specifications. I estimate the reduced form regressions by Nadaraya-Watson estimators and control for the asymptotic bias. I show that the asymptotic test statistic follows a central Chi-squred distribution under the null hypothesis and has power against a sequence of root N-local alternatives. The proposed test statistic works well in a series of finite sample simulations with analytic variances, alleviating the computational burden often involved in bootstrapped inferences. I also show that the test can be straightforwardly extended to semiparametric models, panel data and triangular simultaneous equations frameworks. Hicks-neutral technology implies the substitution pattern of labor and capital in a production function is not affected by technological shocks, first put forth by John Hicks in 1932. In this chapter, I consider the identification and estimation of fully nonparametric firm-level production functions and empirically test the Hicks-neutral productivity in the U.S. manufacturing industry during the period from 1990 to 2011. Firstly, I extend the proxy variable approach to fully nonparametric settings and propose a robust estimator of average output elasticities in non-Hick-neutral scenarios. Secondly, I show that the Hicks-neutral restriction can be converted to the additive separability between inputs and unobservables in a monotonic transformed model for which the proposed testing procedure can be directly applied. It turns out that there is substantial heterogeneity in the nonparametric output elasticities over various counterfactual input amounts. I also find that there were periods in the 90s when the non-Hicks technological shocks occur which coincide with the mass adoption of computing technology. However, the productivity has thereafter become Hicks-neutral into the 2000s. Controlling for sector-specific effects mitigate the non-Hicks-neutrality to some extend. Previous literature on bond rating indicates that credit rating agencies (CRAs) may assign favorable ratings to bond-issuing firms that have a closer relationship. This not only implies the existence of firm-specific unobserved heterogeneity in the rating criteria but also makes some bond/firm characteristics endogenous, which is confirmed by our empirical results. In this chapter, my coauthors and I propose a semiparametric two-step index and location estimator of ordered response models that explicitly incorporates endogenous regressors and correlated random thresholds. We apply our model in the application of assessing bond rating bias of credit rating agencies. Methodologically, we first show that the heterogeneous relative thresholds can be identified using conditional shift restrictions in conjunction with the control variables for the firm-CRA liaison. Then, we illustrate the estimation strategy in a heuristic manner and derive the asymptotic properties of the suggested estimator. In the application, we find significant overrating bias through varying thresholds as the liaison strengthens and those biases display heterogeneous patterns with respect to rating categories.
Four Essays In Econometrics
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Author : Xun (Sean) Lu
language : en
Publisher:
Release Date : 2010
Four Essays In Econometrics written by Xun (Sean) Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Industrial productivity categories.
This dissertation contains four self-contained essays in econometrics. In Chapter 1, we give natural definitions of direct and total structural causality applicable to both structural VARs and recursive structures representing time-series natural experiments. These concepts enable us to forge a previously missing link between Granger (G- ) causality and structural causality by showing that, given a corresponding conditional form of exogeneity, G-causality holds if and only if a corresponding form of structural causality holds. We illustrate with studies of oil and gasoline prices, monetary policy and industrial production, and stock returns and macroeconomic announcements. Chapter 2 provides nonparametric tests for various hypotheses about the effects of a continuous treatment variable in a nonseparable structural equation. These hypotheses have direct policy implications. Specifically, we consider marginal effects and various average effects and test (i) whether these effects depend on the level of treatment; (ii) whether average effects are heterogeneous across different subpopulations defined by covariates; and (iii) whether marginal effects are subject to unobservable heterogeneity. Our tests are based on consistent procedures of Bierens (1982, 1990) and Stinchcombe and White (1998). We apply our tests to study interest rate elasticities of loan demand in microfinance and the impact of education on wages. In Chapter 3, we show that careful examination of the structure determining treatment choice and outcomes is central to proper choice of covariates. We demonstrate how causal diagrams can play a key role in this examination by using these methods to give a detailed analysis of the choice of efficient covariates. Chapter 4 is about a common exercise in empirical studies -"robustness check," where the researcher examines how certain "core" regression coefficient estimates behave when the regression specification is modified by adding or removing regressors. If the coefficients are plausible and robust, this is commonly interpreted as evidence of structural validity. Here, we study when and how one can infer structural validity from coefficient robustness and plausibility. We discuss how critical and non-critical core variables can be properly specified and how non-core variables for the comparison regression can be chosen to ensure that robustness checks are indeed structurally informative.
Identification And Inference For Econometric Models
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Author : Donald W. K. Andrews
language : en
Publisher: Cambridge University Press
Release Date : 2005-07-04
Identification And Inference For Econometric Models written by Donald W. K. Andrews and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-04 with Business & Economics categories.
This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.
Essays In Econometrics And Robust Control
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Author :
language : en
Publisher:
Release Date : 2012
Essays In Econometrics And Robust Control written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
The dissertation consists of two parts. Chapters 1 and 2 concern the econometrics of social networks and chapter 3 concerns robust control. Chapter 1 develops new social interactions identification methods and develops a framework that includes some important existing results as special cases, such as the identification results in Bramoulle, Djebbari, and Fortin (2009, J. Econometrics) (except their proposition 3), the section 4.ii of Blume, Brock, Durlauf, and Ioannides (2011, Handbook of Social Economics) (except their theorems 3 and 5), and Graham (2008, Econometrica). This chapter discovers that diameter is a key network property closely related to identification. The proposed methods are based on the matrix spectral decompositions; they address three canonical identification problems. First, this chapter offers a method of disentangling endogenous and exogenous interactions by the matrix spectral decompositions. Second, this chapter offers a detailed analysis of differencing methods, which solve the endogeneity problem arising from the presence of unobservable group-level heterogeneity (or fixed effects), and provides a method of minimizing the information loss from differencing. Third, this chapter develops an identification method based on the spectral decompositions of covariance matrices for the problem arising from the absence of observable individual-level heterogeneity; Graham's variance contrast method is a special case of this method. Chapter 2 considers linear regression models where individuals interact in a social network so that the disturbances are correlated. A sufficient condition under which the covariance matrices can be consistently estimated is derived. The theory is based on the ideas in White (1984, 2001, Asymptotic Theory for Econometricians). Chapter 3 develops a robust control theory under implementation lag uncertainty. The theory is applied to a Ramsey taxation problem. Implementation lags refer to the lag polynomials of the control variables. The policy maker has estimates of the implementation lag polynomials. He applies the robust control concept of Hansen and Sargent (2008, Robustness) and assumes that there is an adversarial agent minimizes the welfare by choosing implementation lag polynomials that are close to the estimates. The inter-temporal correlations between the control variables and the exogenous state variables are the sources for the adversarial agent to minimize welfare.
Essays On Semi Non Parametric Methods In Econometrics
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Author : Sungwon Lee
language : en
Publisher:
Release Date : 2018
Essays On Semi Non Parametric Methods In Econometrics written by Sungwon Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
My dissertation contains three chapters focusing on semi-/non-parametric models in econometrics. The first chapter, which is a joint work with Sukjin Han, considers parametric/semiparametric estimation and inference in a class of bivariate threshold crossing models with dummy endogenous variables. We investigate the consequences of common practices employed by empirical researchers using this class of models, such as the specification of the joint distribution of the unobservables to be a bivariate normal distribution, resulting in a bivariate probit model. To address the problem of misspecification, we propose a semiparametric estimation framework with parametric copula and nonparametric marginal distributions. This specification is an attempt to ensure robustness while achieving point identification and efficient estimation. We establish asymptotic theory for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effects. Numerical studies suggest the sensitivity of parametric specification and the robustness of semiparametric estimation. This paper also shows that the absence of excluded instruments may result in the failure of identification, unlike what some practitioners believe. The second chapter develops nonparametric significance tests for quantile regression models with duration outcomes. It is common for empirical studies to specify models with many covariates to eliminate the omitted variable bias, even if some of them are potentially irrelevant. In the case where models are nonparametrically specified, such a practice results in the curse of dimensionality. I adopt the integrated conditional moment (ICM) approach, which was developed by Bierens (1982) and Bierens (1990) to construct test statistics. The proposed test statistics are functionals of a stochastic process which converges weakly to a centered Gaussian process. The test has non-trivial power against local alternatives at the parametric rate. A subsampling procedure is proposed to obtain critical values. The third chapter considers identification of treatment effect and its distribution under some distributional assumptions. I assume that a binary treatment is endogenously determined. The main identification objects are the quantile treatment effect and the distribution of the treatment effect. I construct a counterfactual model and apply Manski's approach (Manski (1990)) to find the quantile treatment effects. For the distribution of the treatment effect, I adapt the approach proposed by Fan and Park (2010). Some distributional assumptions called stochastic dominance are imposed on the model to tighten the bounds on the parameters of interest. It also provides confidence regions for identified sets that are pointwise consistent in level. An empirical study on the return to college confirms that the stochastic dominance assumptions improve the bounds on the distribution of the treatment effect.